The growth of the Internet of Things has amplified the need for secure data interactions in cloud-edge ecosystems, where sensitive information is constantly processed across various system layers. Intrusion detection systems are commonly used to protect such environments from malicious attacks. Recently, Federated Learning has emerged as an effective solution for implementing intrusion detection systems, owing to its decentralised architecture that avoids sharing raw data with a central server, thereby enhancing data privacy. Despite its benefits, Federated Learning faces criticism for high communication overhead from frequent model updates, especially in large-scale Cloud-Edge infrastructures. This paper explores Knowledge Distillation to reduce communication overhead in Cloud-Edge intrusion detection while preserving accuracy and data privacy. Experiments show significant improvements over state-of-the-art methods.

Managing complex distributed applications in the Cloud-Edge continuum, including deployment on diverse resources and runtime operations, presents significant challenges. Orchestrators play a key role by automating resource discovery, optimisation, deployment, and life-cycle management while ensuring system performance. This paper introduces Swarmchestrate, a decentralised, application-centric orchestration framework inspired by self-organising Swarms. Our initial findings, based on the implementation in a Cloud-Edge simulator, demonstrate Swarmchestrate's potential, offering insights into resource coordination and optimised allocation for scalable systems.

This paper presents a distributed resource selection mechanism for diverse cloud-edge environments, enabling dynamic and context-aware allocation of resources to meet the demands of complex distributed applications. By distributing the decision-making process, our approach ensures efficiency, scalability, and resilience in highly dynamic cloud-edge environments where centralised coordination becomes a bottleneck. The proposed mechanism aims to function as a core component of a broader, distributed, and self-organising orchestration system that facilitates the intelligent placement and adaptation of applications in real-time. This work leverages a consensus-based mechanism utilising local knowledge and inter-agent collaboration to achieve efficient results without relying on a central controller, thus paving the way for distributed orchestration. Our results indicate that computation time is the key factor influencing allocation decisions. Our approach consistently delivers rapid allocations without compromising optimality or incurring additional cost, achieving timely results at scale where exhaustive search is infeasible and centralised heuristics run up to 30 times slower.

With the emergence of Internet of Things (IoT) devices collecting large amounts of data at the edges of the network, a new generation of hyper-distributed applications is emerging, spanning cloud, fog, and edge computing resources.The automated deployment and management of such applications requires orchestration tools that take a deployment descriptor (e.g.Kubernetes manifest, Helm chart or TOSCA) as input, and deploy and manage the execution of applications at run-time.While most deployment descriptors are prepared by a single person or organisation at one specific time, there are notable scenarios where such descriptors need to be created collaboratively by different roles or organisations, and at different times of the application's life cycle.An example of this scenario is the modular development of digital twins, composed of the basic building blocks of data, model and algorithm.Each of these building blocks can be created independently from each other, by different individuals or companies, at different times.The challenge here is to compose and build a deployment descriptor from these individual components automatically.This paper presents a novel solution to automate the collaborative composition and generation of deployment descriptors for distributed applications within the cloud-to-edge continuum.The implemented solution has been prototyped in over 25 industrial use cases within the DIGITbrain project, one of which is described in the paper as a representative example.

The serverless execution model allows application developers to deploy their software using tiny functions with zero administration, no handling of resource provisioning, monitoring and scaling. Due to such advantages, the serverless model emerged as a new promising paradigm, where pay as you go offerings can be found by all public cloud providers. However, these offerings encourage vendor lock-in. This paper aims to address the vendor lock-in issue using a novel framework that combines the strength of an agnostic infrastructure configuration based on the Constructs Programming Model, and the creation of abstraction layers that supports function logic by handling provider-specific integration. The proposed framework abstracts away the specificities and complications of the various underlying serverless platforms and the application developers are only required to provide their specific functions. The evaluation consists of the deployment of a benchmark application across different cloud providers to demonstrate the ease and flexibility of the framework.

Abstract Science gateways have been widely utilized by a large number of user communities to simplify access to complex distributed computing infrastructures. While science gateways are still becoming increasingly popular and the number of user communities is growing, the fast and efficient creation of new science gateways and the flexibility to deploy these gateways on‐demand on heterogeneous computational resources, remain a challenge. Additionally, the increase in the number of users, especially with very different backgrounds, requires intuitive embedded e‐learning tools that support all stakeholders to find related learning material and to guide the learning process. This paper introduces a novel science gateway framework that addresses these challenges. The framework supports the creation, publication, selection, and deployment of cloud‐based reference architectures that can be automatically instantiated and executed even by nontechnical users. The framework also incorporates a knowledge repository exchange and learning module that provides embedded e‐learning support. To demonstrate the feasibility of the proposed solution, two scientific case studies are presented based on the requirements of the plasmasphere, ionosphere, and thermosphere research communities.

The proliferating adoption of the Internet of Things (IoT) paradigm has fuelled the need for more efficient and resilient access control solutions that aim to prevent unauthorized resource access. The majority of existing works in this field follow either a centralized approach (i.e. cloud-based) or an architecture where the IoT devices are responsible for all decision-making functions. Furthermore, the resource-constrained nature of most IoT devices make securing the communication between these devices and the cloud using standard cryptographic solutions difficult. In this paper, we propose a distributed access control architecture where the core components are distributed between fog nodes and the cloud. To facilitate secure communication, our architecture utilizes a Certificateless Hybrid Signcryption scheme without pairing. We prove the effectiveness of our approach by providing a comparative analysis of its performance in comparison to the commonly used cloud-based centralized architectures. Our implementation uses Azure – an existing commercial platform, and Keycloak – an open-source platform, to demonstrate the real-world applicability. Additionally, we measure the performance of the adopted encryption scheme on two types of resource-constrained devices to further emphasize the applicability of the proposed architecture. Finally, the experimental results are coupled with a theoretical analysis that proves the security of our approach.

Summary As cloud adoption increases, so do the number of available cloud service providers. Moving complex applications between clouds can be beneficial—or other times necessary—but achieving this so‐called cloud portability is rarely straightforward. This article presents the adoption of OASIS TOSCA, a standard in the declarative description of cloud applications, to encourage and facilitate cloud portability in MiCADO, an application‐level multi‐cloud orchestration and auto‐scaling framework. The interface to MiCADO is an Application Description Template, which draws from the TOSCA specification to describe an application in MiCADO. The generic design of these templates is presented and their applicability for achieving portability between different container and cloud environments is analysed and evaluated. A proof‐of‐concept where MiCADO serves as the deployment and execution engine for a Science Gateway in Sleep Healthcare is then described. In this proof‐of‐concept, MiCADO facilitates the deployment of a complex healthcare application, which is then moved from one cloud service provider to another with only minimal changes to the template which originally described it. This TOSCA‐based approach to templates in MiCADO encourages movement between clouds by making cloud portability more approachable.

Abstract Automated deployment and run-time management of microservices-based applications in cloud computing environments is relatively well studied with several mature solutions. However, managing such applications and tasks in the cloud-to-edge continuum is far from trivial, with no robust, production-level solutions currently available. This paper presents our first attempt to extend an application-level cloud orchestration framework called MiCADO to utilise edge and fog worker nodes. The paper illustrates how MiCADO-Edge can automatically deploy complex sets of interconnected microservices in such multi-layered cloud-to-edge environments. Additionally, it shows how monitoring information can be collected from such services and how complex, user- defined run-time management policies can be enforced on application components running at any layer of the architecture. The implemented solution is demonstrated and evaluated using two realistic case studies from the areas of video processing and secure healthcare data analysis.

Network on Chip (NoC) is a communication framework for the Multiprocessor System on Chip (MPSoC). It is a router‐based communication system. In NoC architecture, nodes of MPSoC are communicating through the network. Different routing algorithms have been developed by researchers, e.g., XY, intermittent XY, DyAD, and DyXY. The main problems in these algorithms are congestion and faults. Congestion and faults cause delay, which degrades the performance of NoC. A congestion‐aware algorithm is used for the distribution of traffic over NoC and for the avoidance of congestion. In this paper, a congestion‐aware routing algorithm is proposed. The algorithm works by sending congestion information in the data packet. The algorithm is implemented on a 4 × 4 mesh NoC using FPGA. The proposed algorithm decreases latency, increases throughput, and uses less bandwidth in sharing congestion information between routers in comparison to the existing congestion‐aware routing algorithms.